Baidu Search Ads (a.k.a. “Phoenix Nest”) is one of the most important revenue sources for Baidu [1]}, [2]}, [3]}. In the search industry, sponsored online advertising produces many billions of dollar revenues for online advertisers. Since around 2017, Baidu Search Ads has been undergoing several major upgrades by incorporating the rapid-growing technologies in near neighbor search, machine learning, and systems. For example, [4]} reported new architectures for distributed GPU-based parameter servers which have replaced the MPI-based system for training CTR models. [1]} described the widespread use of approximate near neighbor search (ANNS) and maximum inner product search (MIPS) [6]}, [7]} to substantially improve the quality of ads recalls in the early stage of the pipeline of Baidu's ads system. In recent years, Baidu's short-form video recommendations [8]} and video-based search ads have achieved great progress [9]}, [10]}, [11]}. In this paper, we introduce the technology for a representative project which has significantly boosted Baidu's video-based ads revenues.
